Ablation Experiment on NWPU-VHR-10.

Remote sensing object detection (RSOD) is highly challenging due to large variations in object scales. Existing deep learning-based methods still face limitations in addressing this challenge. Specifically, reliance on stride convolutions during downsampling leads to the loss of object information, and insufficient context-aware modeling capability hampers full utilization of object information at different scales. To address these issues, this paper proposes a Haar wavelet-based Attention Network (HWANet). The model includes a Low-frequency Enhanced Downsampling Module (LEM), a Haar Frequency Domain Self-attention Module (HFDSA), and a Spatial Information Interaction Module (SIIM). Specifically, LEM employs the Haar wavelet transform to downsample feature maps and enhances low-frequency components, mitigating the loss of object information at different scales. The HFDSA module integrates Haar wavelet transform and explicit spatial priors, reducing computational complexity while enhancing the capture of image spatial structures. Meanwhile, the SIIM module facilitates interactions among information at different levels, enabling multi-level feature integration. Together, SIIM and HFDSA strengthen the model’s context-aware modeling capability, allowing full utilization of multi-scale information. Experimental results show that HWANet achieves 93.1% mAP50 on the NWPU VHR-10 dataset and 99.1% mAP50 on the SAR-Airport-1.0 dataset, with only 2.75M parameters, outperforming existing methods.